Bibliographic record
Abstract
Abstract Research question/issue Innovation has been a constant feature of the tales of transition and transformation. State ownership as the engine of innovation and technological change may be juxtaposed with the “liability of stateness” and the notion that “privatization works.” This study seeks to investigate the relationship between legal ownership and innovation inputs and outputs, while accounting for the moderating effect of institutional quality on this relationship. Research findings/insights We exploit unique data from a very large‐scale panel survey of enterprises (65,750 firms between 2006 and 2014) in Vietnam, a fast‐growing but understudied transition country, and apply advanced methodologies that control for the endogeneity of institutions. Our findings point to the continued dominance of state‐owned enterprises (SOEs) in innovation activities in Vietnam. However, the returns to innovation in SOEs accrue only up to a point and improving institutional quality serves to diminish their advantage over privately‐owned enterprises (POEs) and to level the playing field. Theoretical/academic implications We employ an integrated framework that develops predictions from resource dependence, agency, and institutional theories to explore the direct and contingent influences of ownership and institutional quality on firm‐level innovation activities. Our study contributes to the growing literature on state ownership and innovation in transition and emerging economies and the recent calls for greater attention to local institutional context and revising the existing theories on “state underperformance.” Practitioner/policy implications This study offers insights to policy makers in enhancing the quality of local institutions. Higher‐quality institutions moderate the advantages state ownership confers and ameliorate the disadvantages associated with private ownership.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".